Large Model Era: Deep Learning in Osteoporosis Drug Discovery

Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in a decline in patients' life quality. In response to the increased incidence of osteoporosis, related drug discovery has attracted more and more attention, but it is often faced with challenges due to long development cycle and high cost. Deep learning with powerful data processing capabilities has shown significant advantages in the field of drug discovery. With the development of technology, it is more and more applied to all stages of drug discovery. In particular, large models, which have been developed rapidly recently, provide new methods for understanding disease mechanisms and promoting drug discovery because of their large parameters and ability to deal with complex tasks. This review introduces the traditional models and large models in the deep learning domain, systematically summarizes their applications in each stage of drug discovery, and analyzes their application prospect in osteoporosis drug discovery. Finally, the advantages and limitations of large models are discussed in depth, in order to help future drug discovery.